Synchronous Binocular Visible Light Parallax Map for Face Detection

Zhang, Wen Xing; Yang, Zhen Kai; Liu, Wen Jing; Wang, Jingguo

doi:10.1109/icisce50968.2020.00278

2020 7th International Conference on Information Science and Control Engineering (ICISCE) 2020

DOI: 10.1109/icisce50968.2020.00278

|View full text |Cite

Synchronous Binocular Visible Light Parallax Map for Face Detection

Wen Xing Zhang

Zhen Kai Yang

Wen Jing Liu

et al.

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2022

Publication Types

Select...

Article1

Relationship

Self Cite0

Independent1

Authors

Journals

Cited by 1 publication

(1 citation statement)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…There is plenty of research available in face recognition. Some of them are based on Machine learning [6][7][8][9][10], image processing [10][11][12]. The next step is data collection.…”

mentioning

confidence: 99%

Design of an Efficient Face Recognition system using Deep Learning Technique

Rathnala

Patnaik

Sura

et al. 2022

IJEER

View full text Add to dashboard Cite

Greater reliance on smart and portable electronic devices demands engineers to provide solutions with better performance and minimized demerits. Face Recognition involves the method of associating and confirming the faces. It is fit for distinguishing, following, recognizing, or checking human appearances from a picture or video caught utilizing an advanced camera. Feature extraction is the most significant stage for the achievement of the face recognition framework. The different ways of implementing this project depends on the programming language or algorithms used such as MATLAB, OpenCV, visual basics C#, Viola-Jones algorithm and many more while the core functioning remains the same. In this work, we have implemented face recognition in 3 phases, Phase1 consists of detecting faces and collecting images IDs, Phase 2 involves training the Recognizer and Separating interesting elements and the final phase includes grouping them and putting away in XML records.

show abstract

“…There is plenty of research available in face recognition. Some of them are based on Machine learning [6][7][8][9][10], image processing [10][11][12]. The next step is data collection.…”

mentioning

confidence: 99%

Design of an Efficient Face Recognition system using Deep Learning Technique

Rathnala

Patnaik

Sura

et al. 2022

IJEER

View full text Add to dashboard Cite

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Synchronous Binocular Visible Light Parallax Map for Face Detection

Cited by 1 publication

References 8 publications

Design of an Efficient Face Recognition system using Deep Learning Technique

Design of an Efficient Face Recognition system using Deep Learning Technique

Contact Info

Product

Resources

About